Top AI Researcher on GPT 4.5, DeepSeek and Agentic RAG | Douwe Kiela, CEO, Contextual AI
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What are the latest thoughts on GPT‑4.5, DeepSeek and the test‑time compute shift?
Welcome back to the Mad Podcast. Today we have a very special conversation with Dow Kieler, the CEO of Contextual AI, a startup that has raised $100 million to help bring to the enterprise what's known as retrieval augmented generation, or RAG, a fundamentally important technique to make AI accurate and reliable. Dow is an AI researcher by background and actually one of the inventors of RAG. After getting his PhD in computer science at Cambridge, Dow became a research scientist at Facebook Fair. He then became head of research at Hugging Face before starting contextual AI in 2023, while also teaching as an adjunct professor at Stanford University. We started the conversation with Dow's thoughts on the latest AI model innovations, including GPT-4.5, Sonnet 3.7, and of course DeepSeek.
I think some people are maybe a little bit underwhelmed. Expectations are are also getting maybe a little bit inflated uh with all of these releases.
We then did a very educational deep dive into the fundamentals of RAG.
RAG is actually a very simple idea. It's answering a very basic question, which is how do I get the language model to work on top of data that it was not trained on?
And then talked about how contextual is helping usher the next era of rag, agentic rag.
We're a platform for rag agents, and you can specialize these rag agents for different use cases using the platform very easily.
We close with a few thoughts on the reality of deploying AI in the enterprise today.
We had people ask us when are we getting to a hundred percent accuracy? And I had to give them the bad news that probably never.
Now Dow is perhaps the leading authority in the world on the topic of RAG as he bridges both AI research and real world deployments. So please enjoy this wonderful conversation with him. Hey Dow, welcome.
Hi, thanks for having me.
Uh so I thought a fun way to start the conversation would be to riff uh on the crazy pace of releases over the last couple of weeks. So we're recording this the day after GPT 4.5 was was released. So what was formerly known as Orion uh and um uh now the largest uh open AI model uh that was released, even though they don't disclose the number Parameters. And obviously, before that, there was uh Claude Sonnet 3.7 and then Grog 3, and then you know, going all the way back to January of this year, deep research operator, all those things. So I'm curious, maybe with your uh AI researcher hat on uh what do you make of all of this? What catches your imagination? What do you think is is more or less interesting?
Just saying
Yeah, it's uh exciting times, right? There there's so much uh happening, it's hard to keep up. But um Yeah, I I I think the the new model releases are interesting. I think some people are maybe a little bit underwhelmed uh in terms of uh you know expectations are are also getting maybe a little bit inflated uh with all of these releases. For me, the most exciting thing by far is Deep Seek. uh where where that that really I think changed the narrative in the AI ecosystem around what's possible and who actually is an incumbent and what is the moat that some of these companies have. Um so um yeah, I I think it it's really great for for the world that that we have uh kind of an existence proof now that it's actually not that hard to to do this.
Um and so you don't need to invest all that much in in data and you can use synthetic data and and and get a pretty good model out of that. So that that's that's really exciting.
And to your point about um some level of uh disappointment or mixed feeling. So in in particular, the early vote for what it's worth on GPT four point five does indeed seem to be a little mixed. And uh, you know, with people saying that um actually it may not be better than four. Sort of the same thing, by the way, for three dot seven Sonnet, some people are saying, well that you know, for certain use cases, especially the more precise ones, may not be as good as three point five. What do you make of that in a context where uh especially for po four point five, there's been like that whole discussion around scaling laws and whether we were sort of hitting a wall there.
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Chapters
6 chapters
1
What are the latest thoughts on GPT‑4.5, DeepSeek and the test‑time compute shift?
0:00–7:22
2
How does the US‑China AI rivalry influence model development and hype?
7:22–11:35
3
What is Retrieval‑Augmented Generation (RAG) and how did the original paper originate?
11:35–21:09
4
Why did the Transformers paper feel underwhelming at first, and what changed?
21:09–32:02
5
How does the core RAG architecture work and why is it better than fine‑tuning or long context windows?
32:02–46:29
6
What are RAG 2.0, Agentic RAG and the role of synthetic data in self‑learning systems?
46:29–50:43
Speakers
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